dat <- read.csv("data/evolucion_gripe_covid.csv")
gripe <- ts(dat$sdgripal, start=c(2020, 40), frequency=52)
result_gripe <- auto.fit.arima(gripe, plot_result = TRUE)
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Series: serie 
ARIMA(2,0,1) with non-zero mean 

Coefficients:
         ar1      ar2      ma1      mean
      1.7011  -0.8606  -0.7378  233.5599
s.e.  0.1023   0.0832   0.1715   10.7434

sigma^2 = 2245:  log likelihood = -299.83
AIC=609.66   AICc=610.83   BIC=619.87
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Falla la hipótesis de normalidad sobre los residuos.
El modelo es válido pero los intervalos de predicción basados en la
dist. asintótica no son válidos
------------------------------------------------------------------------------------------
|                                      MODELO FINAL                                      |
------------------------------------------------------------------------------------------
Series: serie 
ARIMA(2,0,1) with non-zero mean 

Coefficients:
         ar1      ar2      ma1      mean
      1.7011  -0.8606  -0.7378  233.5599
s.e.  0.1023   0.0832   0.1715   10.7434

sigma^2 = 2245:  log likelihood = -299.83
AIC=609.66   AICc=610.83   BIC=619.87
result_gripe$fig_serie <- result_gripe$fig_serie %>% layout(width=840, height=700)
result_gripe$fig_serie